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April 2014

Motivating and Visualizing Recursion in Python: Three Flavors

Yesterday, I encountered some great instructional posts on programming, Python and the IPython Notebook.

I thought it would be fun to experiment with all 3: implementing the recursive C function used by Gustavo Duarte in Python, doing so inside PythonTutor (which can generate an iframe) and then embedding the PythonTutor iframe inside an IPython Notebook, which I would then embed in this blog post.

Unfortunately, I haven't achieved the trifecta: I couldn't figure out how to embed the PythonTutor iframe inside an IPython Notebook, so I will embed both of them separately here in this blog post. Across the collection, 3 flavors of visualizing recursion are shown:

  • simple print statement output tracing the call stack (ASCII version)
  • a static call stack image created by Gustavo
  • a dynamic call stack created automatically by PythonTutor

I'll start out with embedding Motivating and Visualizing Recursion in Python, an IPython Notebook I created to interweave text, images and code in summarizing Gustavo Duarte's compelling critique and counter-proposal for how best to teach and visualize recursion, and reimplementing his examples in Python.

Next, I'll embed an iframe for visualizing recursion in Python, providing a snapshot of its dynamic execution and visualization within PythonTutor:

I really like the way that PythonTutor enables stepping through and visualizing the call stack (and other elements of the computation). It may not be visible in the iframe above (you have to scroll to the right to see it), so I'll include a snapshot of it below.

Visualizing_Recursion_in_Python_Tutor

If anyone knows how to embed a PythonTutor iframe within an IPython Notebook, please let me know, as I'd still welcome the opportunity to achieve a trifecta ... and I suspect that combining these two tools would represent even more enhanced educational opportunities for Pythonistas.


Python for Data Science: A Rapid On-Ramp Primer

IpythonIn my last post, I was waxing poetic about an IPython Notebook demonstration that was one of my highlights from Strata 2014:

"this one got me the most excited about getting home (or back to work) to practice what I learned"

Well, I got back to work, and learned how to create an IPython Notebook. Specifically, I created one to provide a rapid "on-ramp" for computer programmers who are already familiar with basic concepts and constructs in other programming language to learn enough about Python to effectively use the Atigeo xPatterns analytics framework (or other data science tools). The Notebook also includes some basic data science concepts, utilizing material I'd earlier excerpted in a blog post in which I waxed poetic about the book Data Science for Business, by Foster Provost and Tom Fawcett, and other resources I have found useful in articulating the fundamentals of data science.

nltk_book_cover.gif The rapid on-ramp approach was motivated, in part, by my experience with the Natural Language Toolkit (NLTK) book, which provides a rapid on-ramp for learning Python in conjunction with the open-source NLTK library to develop programs using natural language processing techniques (many of which involve machine learning). I find that IPython Notebooks are such a natural and effective way of integrating instructional information and "active" exercises that I wish I'd discovered it back when I was teaching courses using Python at the University of Washington (e.g., what came to be known as the socialbots course). I feel like a TiVO fanatic now, wanting to encourage anyone and everyone sharing any knowledge about Python to use IPython Notebooks as a vehicle for doing so.

I piloted an initial version of the Python for Data Science notebook during an internal training session for software engineers who had experience with Java and C++ a few weeks ago, and it seemed to work pretty well. After the Strata 2014 videos were released, I watched Olivier Grisel's tutorial on Introduction to Machine Learning with IPython and scikit-learn, and worked through the associated parallel_ml_tutorial notebooks he posted on GitHub. I updated my notebook to include some additional aspects of Python that I believe would be useful in preparation for that tutorial.

Not only was this my first IPython Notebook, but I'm somewhat embarrassed to admit that the Python for Data Science repository represents my first contribution to GitHub. When I was teaching at UW, I regularly encouraged students to contribute to open source projects. Now I'm finally walking the talk ... better late than never, I suppose.

In any case, I've uploaded a link to the repository on the IPython Notebook Viewer (NBViewer) server - "a simple way to share IPython Notebooks" - so that the Python for Data Science notebook can be viewed in a browser, without running a local version of IPython Notebook (note that it may take a while load, as it is a rather large notebook).

I'll include the contents of the repo's README.md file below. Any questions, comments or other feedback is most welcome.

This short primer on Python is designed to provide a rapid "on-ramp" for computer programmers who are already familiar with basic concepts and constructs in other programming languages to learn enough about Python to effectively use open-source and proprietary Python-based machine learning and data science tools.

The primer is spread across a collection of IPython Notebooks, and the easiest way to use the primer is to install IPython Notebook on your computer. You can also install Python, and manually copy and paste the pieces of sample code into the Python interpreter, as the primer only makes use of the Python standard libraries.

There are three versions of the primer. Two versions contain the entire primer in a single notebook:

The other version divides the primer into 5 separate notebooks:

  1. Introduction
  2. Data Science: Basic Concepts
  3. Python: Basic Concepts
  4. Using Python to Build and Use a Simple Decision Tree Classifier
  5. Next Steps

There are several exercises included in the notebooks. Sample solutions to those exercises can be found in two Python source files:

  • simple_ml.py: a collection of simple machine learning utility functions
  • SimpleDecisionTree.py: a Python class to encapsulate a simplified version of a popular machine learning model

There are also 2 data files, based on the mushroom dataset in the UCI Machine Learning Repository, used for coding examples, exploratory data analysis and building and evaluating decision trees in Python:

  • agaricus-lepiota.data: a machine-readable list of examples or instances of mushrooms, represented by a comma-separated list of attribute values
  • agaricus-lepiota.attributes: a machine-readable list of attribute names and possible attribute values and their abbreviations